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Performance analysis of superimposed training-based cooperative spectrum sensing
Ref: CISTER-TR-171203       Publication Date: 21 to 23, Feb, 2018

Performance analysis of superimposed training-based cooperative spectrum sensing

Ref: CISTER-TR-171203       Publication Date: 21 to 23, Feb, 2018

Abstract:
Superimposed training (ST) technique can be used at primary users’ transmitters to improve parameter estimation tasks (e.g. channel estimation) at primary users’ receivers at the time the total available bandwidth is used for data transmission. The exploitation of the ST sequence in the context of cognitive radio networks leads to a significant increase in the detection performance of secondary users operating in the very low signal-to-noise ratio region. Hence, a smaller number of samples are required for sensing. In this paper, the performance of ST-based spectrum sensing in a cooperative centralized cognitive radio network with soft-decision fusion is studied. Furthermore a throughput analysis is carried out to quantify the benefits of using ST in the context of cognitive radio for both primary and secondary users.

Authors:
Lizeth Lopez-Lopez
,
Marco Cardenas-Juarez
,
Enrique Stevens-Navarro
,
Abel Garcia
,
Rafael Aguilar-Gonzalez
,
Ramiro Robles


Accepted in 28th International Conference on Electronics, Communications and Computers (CONIELECOMP 2018).
Puebla, Mexico.



Record Date: 6, Dec, 2017